🌾 AI Farm Management Assistant

Advanced AI-powered farming assistant with production-grade architecture, memory management, and sequential planning capabilities.

🏗️ Project Structure

FARM_AGENT/
├── src/                        # Root for all source code
│   ├── __init__.py
│   ├── core/                   # Core business logic
│   │   ├── __init__.py
│   │   ├── agents.py           # Agent implementations
│   │   ├── callbacks.py        # Callback logic
│   │   ├── guardrails.py       # Guardrail rules and checks
│   │   ├── memory.py           # Memory management
│   │   ├── planning.py         # Planning agents
│   │   └── processors.py       # Data processing logic
│   ├── tools/                  # Reusable tools and utilities
│   │   ├── __init__.py
│   │   ├── tools.py            # Tool definitions (weather, market, etc.)
│   │   └── utils.py            # Shared helper functions
│   ├── config/                 # Configuration management
│   │   ├── __init__.py
│   │   ├── config.py           # Configuration loading
│   │   ├── .env                # Environment variables
│   │   └── env.example         # Example environment file
│   ├── observability/          # Monitoring and logging
│   │   ├── __init__.py
│   │   ├── logging_setup.py    # Logging configuration
│   │   └── observability.py    # Observability metrics/tracing
│   └── models/                 # Data models
│       ├── __init__.py
│       └── models.py
├── main.py                     # Application entry point
├── pyproject.toml              # Project configuration
├── README.md                   # This file
├── LICENSE                     # License information
├── ARCHITECTURE.md             # Architecture documentation
└── OBSERVABILITY.md            # Observability documentation

🚀 Features

• 🧠 Memory Management: Sliding window conversation memory with farmer profile building
• 🎯 Sequential Planning: Production-grade planning with quality assurance and reflection
• 🛡️ Guardrails: Agricultural domain enforcement and safety checks
• 🌤️ Weather Integration: Real-time weather data and forecasts
• 💰 Market Prices: Live mandi prices for agricultural commodities
• 📊 Data Processing: Google Sheets integration for customer data
• 🌾 RAG Knowledge: Agricultural knowledge base with vector search
• 📈 Observability: Laminar tracing for performance monitoring
• 🔄 Session Management: Persistent conversations across interactions

🏃‍♂️ Quick Start

1. Clone and Setup:

   git clone <repository-url>
   cd farm_agent

2. Configure Environment:

   # Copy environment template
   cp src/config/env.example src/config/.env
   
   # Edit with your credentials
   nano src/config/.env

3. Install Dependencies:

   pip install -r requirements.txt

4. Run the Assistant:

   python main.py

🔧 Configuration

The system uses environment variables for configuration. Key settings:

• PROJECT_ID: Google Cloud Project ID
• LOCATION: Google Cloud region (e.g., us-east4)
• MODEL: Gemini model name (default: gemini-2.0-flash-001)
• RAG_CORPUS_NAME: RAG corpus resource name
• LMNR_PROJECT_API_KEY: Laminar observability API key (optional)
• SHEETS_BASE_URL: Google Sheets API endpoint

🎯 Usage Examples

Sequential Planning Queries

"Create a complete plan to manage pest outbreak in my cotton field"
"How do I convert 5 acres from rice to organic vegetable farming?"
"Step-by-step process for setting up drip irrigation system"

Information Queries

"What's the weather forecast for Punjab?"
"Current market prices for basmati rice"
"Show me customer data for ID: 12345"
"Best practices for organic pest management"

🏛️ Architecture Highlights

• Modular Design: Clean separation of concerns with src/ structure
• Memory System: ConversationMemoryManager with sliding window + summarization
• Planning Intelligence: Sequential planning with quality reflection
• Safety First: Multi-layer guardrails and input validation
• Production Ready: Comprehensive error handling and resource cleanup

📝 Development

The new structure provides clear separation:

• Core Logic (src/core/): Business logic, agents, memory, planning
• Tools & Utils (src/tools/): Reusable components and utilities
• Configuration (src/config/): All configuration and environment management
• Models (src/models/): Data models and schemas
• Observability (src/observability/): Logging, tracing, monitoring

🛠️ Technical Stack

• AI Framework: Google ADK (Agent Development Kit)
• LLM: Google Gemini 2.0 Flash
• Memory: Production-grade conversation management
• Planning: Producer-Critic reflection pattern
• Observability: Laminar tracing integration
• Data: Pydantic models with validation

📚 Documentation

• Architecture Guide
• Observability Setup
• API Documentation

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Built with ❤️ for farmers, powered by Google ADK and production-grade AI patterns.